Apply scvi-tools probabilistic models to single-cell omics data in AnnData format.
npx skills add https://github.com/ovachiever/droid-tings --skill scvi-tools-ovachiever
Please help me install this Agent Skill. Skill: scvi-tools Source: https://github.com/ovachiever/droid-tings/tree/main/skills/scvi-tools Command: npx skills add https://github.com/ovachiever/droid-tings --skill scvi-tools-ovachiever
This Skill provides scvi-tools frameworks for probabilistic single-cell analysis, batch correction, and complex multi-omics modeling with scVI, SCANVI, TOTALVI, and more.
Register your AnnData object, pick a model (SCVI/SCANVI), train, and then extract latent representations for clustering and DE analysis.
High-intent search queries and answers about installing and using this skill.
Batch correction in scRNA-seq uses probabilistic models like scVI to integrate data across studies and experimental conditions. Register your AnnData object with batch covariates, train the model, and extract corrected latent representations for downstream analysis.
Yes. scvi-tools provides TOTALVI and MULTIVI for multimodal integration across protein, RNA, and chromatin modalities. These models handle modality-aware likelihoods and generate unified latent representations for joint analysis.
Bayesian differential expression testing in scvi-tools provides uncertainty quantification on latent representations. Train a probabilistic model, then extract posterior samples to compute credible intervals and effect sizes without multiple-testing corrections.
GPU acceleration is required for efficient training of variational autoencoders on large single-cell datasets. The setup-train-extract API leverages GPU compute; CPU execution is feasible for small datasets but significantly slower.
CELLASSIGN within scvi-tools enables marker-based cell type annotation by learning a probabilistic mapping between genes and cell types. Combined with scVI latent representations, it handles uncertainty in assignment across batches and conditions.
Yes. scvi-tools supports spatial transcriptomics analysis through its multimodal framework, enabling integration of spatial location information with gene expression for joint tissue mapping and cell typing across modalities.
AlterLab-IEUTrain scvi-tools VAEs for batch-corrected single-cell omics analysis.
KetomihineMap single-cell datasets into reference atlases using scArches.
jaechang-hitsProvides scVI/scANVI/totalVI/scARCHES modeling for scRNA-seq and CMI-seq data in single-cell omics analysis.
jaechang-hitsRemove technical batch effects from PCA embeddings in single-cell omics data.
